I am not giving up, and I hope I will still achieve some big success.
In the shortest term... I have a baby now, which turned my life upside down a bit, so I need to solve some logistic problems first (e.g. to buy a new flat) and get used to the new situation. It might take a year. -- Not complaining here; I always wanted to have children, but it's taking time and energy and money, so my options are now more limited than usual. I believe it will be okay in a few months, but today, I am rather busy and tired. Also, having a family limits my options; for example if I would decide that moving to another city would make my life better, it is no longer only my own decision. My hands are a bit more tied than they would be if I were 25 again.
I still didn't give up completely on starting a rationalist community in my own city, and I have two specific plans. (1) These days I am finishing the translation of the LW Sequences book; when it is ready, I will distribute it freely and try to make it popular, and hope that people who enjoy it will contact me. (2) In September, I plan to do some rationality "lectures" (advertising for LW and for the translated book) on at least one high school, and one university.
I will probably not do anything scientific, ever; that train has already gone. Cannot compete with 20-years olds with fresh brains and fresh memories of their university lectures, who don't have a family to feed. It would be wiser to focus fully on my personal life and making money, because that's what I have to do anyway. -- The current plan is writing computer games, because the entry costs are almost zero, and I can do it at home in the evenings when the baby sleeps. (I have to keep the day job to pay bills.) Later, when the baby grows up and starts attenting school, I may try something more ambitious.
But still, even if my plans succeed and I live till 80, I will not be able to do as much as in the hypothetical parallel universe where I would find a LW community as a teenager (and also live till 80). But it will still be better than yet another parallel universe where LW doesn't exist at all or where I am somehow unable to find it.
It is so painful to have an easily available possible world in which you find LessWrong earlier than in the real world. I ran into LW/OB five times since I was 16 and didn't stick around until I was 21. I can't imagine what I would be like with five years of exposure to the important things that I've been exposed to in the past six months, as well as having grown alongside the community, seeing as how I came around near the time that LW began.
Is statistics beyond introductory statistics important for general reasoning?
Ideas such as regression to the mean, that correlation does not imply causation and base rate fallacy are very important for reasoning about the world in general. One gets these from a deep understanding of statistics 101, and the basics of the Bayesian statistical paradigm. Up until one year ago, I was under the impression that more advanced statistics is technical elaboration that doesn't offer major additional insights into thinking about the world in general.
Nothing could be further from the truth: ideas from advanced statistics are essential for reasoning about the world, even on a day-to-day level. In hindsight my prior belief seems very naive – as far as I can tell, my only reason for holding it is that I hadn't heard anyone say otherwise. But I hadn't actually looked advanced statistics to see whether or not my impression was justified :D.
Since then, I've learned some advanced statistics and machine learning, and the ideas that I've learned have radically altered my worldview. The "official" prerequisites for this material are calculus, differential multivariable calculus, and linear algebra. But one doesn't actually need to have detailed knowledge of these to understand ideas from advanced statistics well enough to benefit from them. The problem is pedagogical: I need to figure out how how to communicate them in an accessible way.
Advanced statistics enables one to reach nonobvious conclusions
To give a bird's eye view of the perspective that I've arrived at, in practice, the ideas from "basic" statistics are generally useful primarily for disproving hypotheses. This pushes in the direction of a state of radical agnosticism: the idea that one can't really know anything for sure about lots of important questions. More advanced statistics enables one to become justifiably confident in nonobvious conclusions, often even in the absence of formal evidence coming from the standard scientific practice.
IQ research and PCA as a case study
The work of Spearman and his successors on IQ constitute one of the pinnacles of achievement in the social sciences. But while Spearman's discovery of IQ was a great discovery, it wasn't his greatest discovery. His greatest discovery was a discovery about how to do social science research. He pioneered the use of factor analysis, a close relative of principal component analysis (PCA).
The philosophy of dimensionality reduction
PCA is a dimensionality reduction method. Real world data often has the surprising property of "dimensionality reduction": a small number of latent variables explain a large fraction of the variance in data.
This is related to the effectiveness of Occam's razor: it turns out to be possible to describe a surprisingly large amount of what we see around us in terms of a small number of variables. Only, the variables that explain a lot usually aren't the variables that are immediately visible – instead they're hidden from us, and in order to model reality, we need to discover them, which is the function that PCA serves. The small number of variables that drive a large fraction of variance in data can be thought of as a sort of "backbone" of the data. That enables one to understand the data at a "macro / big picture / structural" level.
This is a very long story that will take a long time to flesh out, and doing so is one of my main goals.